Burkhard Rost mostly deals with Computational biology, Protein structure, Genetics, Protein secondary structure and Protein structure prediction. His Computational biology research integrates issues from Proteome, Protein function prediction, Peptide sequence, Sequence analysis and Membrane protein. The concepts of his Protein structure study are interwoven with issues in Amino acid, Protein–protein interaction, Conserved sequence, Protein folding and Sequence.
His work investigates the relationship between Genetics and topics such as Snap that intersect with problems in Neutral mutation, Supplementary data, Single amino acid and Function. The Protein secondary structure study combines topics in areas such as Bioinformatics, Artificial neural network, Globular protein, Percentage point and Algorithm. His Protein structure prediction research is multidisciplinary, incorporating elements of Data mining and Threading.
The scientist’s investigation covers issues in Computational biology, Structural genomics, Protein structure, Genetics and Protein secondary structure. His research integrates issues of Proteome, Bioinformatics, Peptide sequence, Homology and Sequence analysis in his study of Computational biology. His Structural genomics research includes themes of Domain, Solution structure, Stereochemistry and Protein domain.
Burkhard Rost works mostly in the field of Protein structure, limiting it down to topics relating to Transmembrane domain and, in certain cases, Membrane protein. Genome, Sequence, In silico, Protein–protein interaction and Genomics are among the areas of Genetics where Burkhard Rost concentrates his study. His Protein secondary structure study incorporates themes from Artificial neural network, Artificial intelligence, Algorithm and Protein structure prediction.
Computational biology, Artificial intelligence, Machine learning, Proteome and Protein structure are his primary areas of study. His biological study spans a wide range of topics, including Transmembrane protein, Gene, Homology, Sequence and Protein sequencing. His Proteome research includes elements of Schizosaccharomyces pombe, Proteomics and UniProt.
The study incorporates disciplines such as Evolutionary biology, Protein secondary structure and Protein family in addition to Protein structure. He focuses mostly in the field of Protein secondary structure, narrowing it down to topics relating to Subcellular localization and, in certain cases, Protein structure and function and Artificial neural network. His Genome study is associated with Genetics.
His primary areas of investigation include Computational biology, Artificial intelligence, Machine learning, Protein structure and Biochemistry. His Computational biology research is multidisciplinary, relying on both Genome, Support vector machine, Transmembrane domain and Protein–protein interaction prediction. His work carried out in the field of Artificial intelligence brings together such families of science as Set and Protein–protein interaction.
His Machine learning study combines topics from a wide range of disciplines, such as Training set, Sequence, Protein function, Focus and Protein Interaction Networks. His Protein structure study combines topics in areas such as Glycosylation, Endoplasmic reticulum and Data integration. His study in Deep learning is interdisciplinary in nature, drawing from both Protein secondary structure, Artificial neural network, Target protein, Transfer of learning and Subcellular localization.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Prediction of Protein Secondary Structure at Better than 70% Accuracy
Burkhard Rost;Chris Sander.
Journal of Molecular Biology (1993)
The Transcriptional Landscape of the Mammalian Genome
P. Carninci;T. Kasukawa;S. Katayama;J. Gough.
Twilight zone of protein sequence alignments.
Protein Engineering (1999)
Combining evolutionary information and neural networks to predict protein secondary structure.
Burkhard Rost;Chris Sander.
The PredictProtein server
Burkhard Rost;Jinfeng Liu.
Nucleic Acids Research (2003)
PHD: predicting one-dimensional protein structure by profile-based neural networks.
Methods in Enzymology (1996)
Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles.
Gianluca Pollastri;Darisz Przybylski;Burkhard Rost;Pierre Baldi.
Topology prediction for helical transmembrane proteins at 86% accuracy.
Burkhard Rost;Piero Fariselli;Rita Casadio.
Protein Science (1996)
PHD - AN AUTOMATIC MAIL SERVER FOR PROTEIN SECONDARY STRUCTURE PREDICTION
Burkhard Rost;Chris Sander;Reinhard Schneider.
A large-scale evaluation of computational protein function prediction
Predrag Radivojac;Wyatt T Clark;Tal Ronnen Oron;Alexandra M Schnoes.
Nature Methods (2013)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: